Context Window Optimization: Why Token Budget Is Your Real Limiting Factor

Most people optimize for output quality without realizing the real constraint is input space. Here's what I've learned after testing this across dozens of use cases:

**The Core Problem:**

Context windows aren't infinite. Claude 3.5 gives you 200K tokens, but if you stuff it with:

– Full conversation history

– Massive reference documents

– Multiple system prompts

– Example interactions

You're left with maybe 5K tokens for actual response. The model suffocates in verbosity.

**Three Practical Fixes:**

1. **Hierarchical Summarization** – Don't pass raw docs. Create executive summaries with markers ("CRITICAL", "CONTEXT ONLY", "EXAMPLE"). The model learns to weight tokens differently.

2. **Rolling Context** – Keep only the last 5 interactions, not the entire chat. This is counterintuitive but eliminates noise. Newer context is usually more relevant.

3. **Explicit Token Budgets** – Add this to your system prompt: "You have 4000 tokens remaining. Structure responses accordingly." Forces the model to be strategic.

**Real Example:**

I was passing a 50-page research paper to analyze. First try: 80K tokens wasted on reading, 5K on actual analysis.

Second try: Extracted abstract + 3 key sections. 15K tokens total. Better output quality.

What's your use case? Token budget constraints feel different by domain (research vs coding vs creative writing). Curious what patterns you're hitting.